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With the prosperity and popularity of social tagging communities, developing an enormous amount of user-generated data with modalities has emerged in recent years. Personalized search based on tag-based profiles, an indispensable and prominent way to assist users to access and retrieve their interested resources, has been extensively studied by research communities. In this paper, we revisit the extant...
Short text is prevalent on the Web, but it brings challenges to content analysis methods for the lack of contextual information. Biterm topic model (BTM) is a variant of latent Dirichlet allocation, which effectively infers the latent topic distribution of short text by modeling the generation of biterms in the whole corpus. However, it needs fine-tuning from labels to reduce noise when applied to...
With the extensive growth of social media services, many users express their feelings and opinions through news articles, blogs and tweets/microblogs. To discover the connections between emotions evoked in a user by varied-scale documents effectively, the paper is concerned with the problem of sentiment analysis over online news. Different from previous models which treat training documents uniformly,...
In social based web services systems, some resources gain popularity while others do not. It would be valuable if we can predict the popularity of certain resource. In this work, we study the recipe popularity prediction problem using the Yelp dataset. We investigate various features that can be extracted and help to improve the performance. In particular, we propose to do the sentiment analysis over...
With the development of the Internet, user-generated data has been growing tremendously in Web 2.0 era. Facing such a big volume of resources in folksonomy, people need a method of fast exploration and indexing to find their demanded data. To achieve this goal, contextual information is indispensable and valuable to understand user preference and purpose. In sociolinguistics, context can be mainly...
As an event-based task, Emerging Event Detection (EED) faces the problems of multiple events on the same subject and the evolution of events. Current term weighting schemes for EED exploiting Named Entity, temporal information and Topic Modeling all have their limited utility. In this paper, a new term weighting scheme, which models the sparse aspect, global weight and local weight of each story,...
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